Apparatuses and methods for network resource dimensioning in accordance with differentiated quality of service

ABSTRACT

Aspects include determining whether a utilization of wireless spectrum associated with a guaranteed class of traffic in a network is greater than a first threshold, responsive to the determining indicating that the utilization of the wireless spectrum associated with the guaranteed class of traffic is greater than the first threshold, causing an upgrade of a capacity in the network, and responsive to the determining indicating that the utilization of the wireless spectrum associated with the guaranteed class of traffic is not greater than the first threshold: determining a throughput for a non-guaranteed class of traffic for each cell of a plurality of cells of the network, and responsive to determining that the throughput for the non-guaranteed class of traffic for at least one cell of the plurality of cells is less than a second threshold, causing the upgrade of the capacity in the network. Other embodiments are disclosed.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/868,718 filed May 7, 2020. All sections of the aforementionedapplication are incorporated herein by reference in their entirety.

FIELD OF THE DISCLOSURE

The subject disclosure relates to apparatuses and methods for networkresource dimensioning in accordance with differentiated quality ofservice (QoS).

BACKGROUND

As the world becomes increasingly connected via vast communicationnetworks and communication devices, additional challenges arecreated/generated from the perspective of provisioning and managingnetwork resources. For example, from a perspective of a networkoperator, a policy that favors cost reduction (e.g., cost minimization)while deemphasizing (e.g., disregarding/ignoring) quality of service(QoS) parameters runs a risk of degradation in terms of a user's qualityof experience (QoE). The reduction in QoE may tend to alienate/annoy theuser, potentially to the point that the user may terminate service withthe network operator. On the other hand, a policy that conservativelyallocates resources (e.g., spectrum, bandwidth, etc.) to ensure highlevels of QoS or QoE, without taking into account fine-grain QoSconsiderations, runs a risk of wasteful/unnecessary surplus investment.

BRIEF DESCRIPTION OF THE DRAWINGS

Reference will now be made to the accompanying drawings, which are notnecessarily drawn to scale, and wherein:

FIG. 1 is a block diagram illustrating an exemplary, non-limitingembodiment of a communications network in accordance with variousaspects described herein.

FIG. 2A is a block diagram illustrating an example, non-limitingembodiment of a system functioning within the communication network ofFIG. 1 in accordance with various aspects described herein.

FIG. 2B is a block diagram illustrating an example, non-limitingembodiment of a system functioning within the system of FIG. 2A inaccordance with various aspects described herein.

FIG. 2C depicts an illustrative embodiment of a method for allocatingcells and spectrum in accordance with various aspects described herein.

FIG. 2D depicts an illustrative embodiment of a method forperforming/engaging a simulation in accordance with various aspectsdescribed herein.

FIG. 3 is a block diagram illustrating an example, non-limitingembodiment of a virtualized communication network in accordance withvarious aspects described herein.

FIG. 4 is a block diagram of an example, non-limiting embodiment of acomputing environment in accordance with various aspects describedherein.

FIG. 5 is a block diagram of an example, non-limiting embodiment of amobile network platform in accordance with various aspects describedherein.

FIG. 6 is a block diagram of an example, non-limiting embodiment of acommunication device in accordance with various aspects describedherein.

DETAILED DESCRIPTION

The subject disclosure describes, among other things, illustrativeembodiments for allocating or dimensioning resources associated with acommunication network in accordance with priority tiers/classes ofservice. Other embodiments are described in the subject disclosure.

One or more aspects of the subject disclosure include computing acapacity for each cell of a plurality of cells associated with anetwork, responsive to determining that a utilization of wirelessspectrum associated with a first plurality of classes of traffic in thenetwork is greater than a first threshold, upgrading a capacity in thenetwork, and responsive to determining that the utilization of thewireless spectrum associated with the first plurality of classes oftraffic in the network is less than or equal to the first threshold:computing a capacity for a second plurality of classes of traffic foreach cell of the plurality of cells in accordance with the capacity foreach cell, performing analytical modeling or engaging a simulation todetermine a throughput for each of the second plurality of classes oftraffic for each cell of the plurality of cells in accordance with thecomputing of the capacity for the second plurality of classes oftraffic, and responsive to determining that the throughput for at leastone of the second plurality of classes of traffic for at least one cellof the plurality of cells is less than a second threshold, upgrading thecapacity in the network, wherein the upgrading of the capacity in thenetwork comprises one of: deploying a new cell at a predetermined levelof wireless spectrum, wherein the new cell is not included in theplurality of cells, or increasing a wireless spectrum allocation of afirst cell of the plurality of cells.

One or more aspects of the subject disclosure include responsive todetermining that a utilization of wireless spectrum associated with afirst class of traffic in a network is greater than a first threshold,performing an upgrade of a capacity in the network, and responsive todetermining that the utilization of the wireless spectrum associatedwith the first class of traffic in the network is less than or equal tothe first threshold: computing a capacity for a second class of trafficfor each cell of a plurality of cells of the network, performinganalytical modeling or engaging a simulation to determine a throughputfor the second class of traffic for each cell of the plurality of cellsin accordance with the computing of the capacity for the second class oftraffic, and responsive to determining that the throughput for thesecond class of traffic for at least one cell of the plurality of cellsis less than a second threshold, performing the upgrade of the capacityin the network, wherein the performing of the upgrade of the capacity inthe network comprises one of: deploying a new cell at a predeterminedlevel of wireless spectrum, wherein the new cell is not included in theplurality of cells, or increasing a wireless spectrum allocation of afirst cell of the plurality of cells.

One or more aspects of the subject disclosure include determiningwhether a utilization of wireless spectrum associated with a guaranteedclass of traffic in a network is greater than a first threshold,responsive to the determining indicating that the utilization of thewireless spectrum associated with the guaranteed class of traffic isgreater than the first threshold, causing an upgrade of a capacity inthe network, and responsive to the determining indicating that theutilization of the wireless spectrum associated with the guaranteedclass of traffic is not greater than the first threshold: determining athroughput for a non-guaranteed class of traffic for each cell of aplurality of cells of the network, and responsive to determining thatthe throughput for the non-guaranteed class of traffic for at least onecell of the plurality of cells is less than a second threshold, causingthe upgrade of the capacity in the network, wherein the causing of theupgrade of the capacity in the network comprises: deploying a new cellat a predetermined level of wireless spectrum, wherein the new cell isnot included in the plurality of cells, increasing a wireless spectrumallocation of a first cell of the plurality of cells, or a combinationthereof.

Referring now to FIG. 1, a block diagram is shown illustrating anexample, non-limiting embodiment of a system 100 in accordance withvarious aspects described herein. For example, system 100 can facilitatein whole or in part computing a capacity for each cell of a plurality ofcells associated with a network, responsive to determining that autilization of wireless spectrum associated with a first plurality ofclasses of traffic in the network is greater than a first threshold,upgrading a capacity in the network, and responsive to determining thatthe utilization of the wireless spectrum associated with the firstplurality of classes of traffic in the network is less than or equal tothe first threshold: computing a capacity for a second plurality ofclasses of traffic for each cell of the plurality of cells in accordancewith the capacity for each cell, performing analytical modeling orengaging a simulation to determine a throughput for each of the secondplurality of classes of traffic for each cell of the plurality of cellsin accordance with the computing of the capacity for the secondplurality of classes of traffic, and responsive to determining that thethroughput for at least one of the second plurality of classes oftraffic for at least one cell of the plurality of cells is less than asecond threshold, upgrading the capacity in the network, wherein theupgrading of the capacity in the network comprises one of: deploying anew cell at a predetermined level of wireless spectrum, wherein the newcell is not included in the plurality of cells, or increasing a wirelessspectrum allocation of a first cell of the plurality of cells. System100 can facilitate in whole or in part responsive to determining that autilization of wireless spectrum associated with a first class oftraffic in a network is greater than a first threshold, performing anupgrade of a capacity in the network, and responsive to determining thatthe utilization of the wireless spectrum associated with the first classof traffic in the network is less than or equal to the first threshold:computing a capacity for a second class of traffic for each cell of aplurality of cells of the network, performing analytical modeling orengaging a simulation to determine a throughput for the second class oftraffic for each cell of the plurality of cells in accordance with thecomputing of the capacity for the second class of traffic, andresponsive to determining that the throughput for the second class oftraffic for at least one cell of the plurality of cells is less than asecond threshold, performing the upgrade of the capacity in the network,wherein the performing of the upgrade of the capacity in the networkcomprises one of: deploying a new cell at a predetermined level ofwireless spectrum, wherein the new cell is not included in the pluralityof cells, or increasing a wireless spectrum allocation of a first cellof the plurality of cells. System 100 can facilitate in whole or in partdetermining whether a utilization of wireless spectrum associated with aguaranteed class of traffic in a network is greater than a firstthreshold, responsive to the determining indicating that the utilizationof the wireless spectrum associated with the guaranteed class of trafficis greater than the first threshold, causing an upgrade of a capacity inthe network, and responsive to the determining indicating that theutilization of the wireless spectrum associated with the guaranteedclass of traffic is not greater than the first threshold: determining athroughput for a non-guaranteed class of traffic for each cell of aplurality of cells of the network, and responsive to determining thatthe throughput for the non-guaranteed class of traffic for at least onecell of the plurality of cells is less than a second threshold, causingthe upgrade of the capacity in the network, wherein the causing of theupgrade of the capacity in the network comprises: deploying a new cellat a predetermined level of wireless spectrum, wherein the new cell isnot included in the plurality of cells, increasing a wireless spectrumallocation of a first cell of the plurality of cells, or a combinationthereof.

In particular, in FIG. 1 a communications network 125 is presented forproviding broadband access 110 to a plurality of data terminals 114 viaaccess terminal 112, wireless access 120 to a plurality of mobiledevices 124 and vehicle 126 via base station or access point 122, voiceaccess 130 to a plurality of telephony devices 134, via switching device132 and/or media access 140 to a plurality of audio/video displaydevices 144 via media terminal 142. In addition, communication network125 is coupled to one or more content sources 175 of audio, video,graphics, text and/or other media. While broadband access 110, wirelessaccess 120, voice access 130 and media access 140 are shown separately,one or more of these forms of access can be combined to provide multipleaccess services to a single client device (e.g., mobile devices 124 canreceive media content via media terminal 142, data terminal 114 can beprovided voice access via switching device 132, and so on).

The communications network 125 includes a plurality of network elements(NE) 150, 152, 154, 156, etc. for facilitating the broadband access 110,wireless access 120, voice access 130, media access 140 and/or thedistribution of content from content sources 175. The communicationsnetwork 125 can include a circuit switched or packet switched network, avoice over Internet protocol (VoIP) network, Internet protocol (IP)network, a cable network, a passive or active optical network, a 4G, 5G,or higher generation wireless access network, WIMAX network,UltraWideband network, personal area network or other wireless accessnetwork, a broadcast satellite network and/or other communicationsnetwork.

In various embodiments, the access terminal 112 can include a digitalsubscriber line access multiplexer (DSLAM), cable modem terminationsystem (CMTS), optical line terminal (OLT) and/or other access terminal.The data terminals 114 can include personal computers, laptop computers,netbook computers, tablets or other computing devices along with digitalsubscriber line (DSL) modems, data over coax service interfacespecification (DOCSIS) modems or other cable modems, a wireless modemsuch as a 4G, 5G, or higher generation modem, an optical modem and/orother access devices.

In various embodiments, the base station or access point 122 can includea 4G, 5G, or higher generation base station, an access point thatoperates via an 802.11 standard such as 802.11n, 802.11ac or otherwireless access terminal. The mobile devices 124 can include mobilephones, e-readers, tablets, phablets, wireless modems, and/or othermobile computing devices.

In various embodiments, the switching device 132 can include a privatebranch exchange or central office switch, a media services gateway, VoIPgateway or other gateway device and/or other switching device. Thetelephony devices 134 can include traditional telephones (with orwithout a terminal adapter), VoIP telephones and/or other telephonydevices.

In various embodiments, the media terminal 142 can include a cablehead-end or other TV head-end, a satellite receiver, gateway or othermedia terminal 142. The display devices 144 can include televisions withor without a set top box, personal computers and/or other displaydevices.

In various embodiments, the content sources 175 include broadcasttelevision and radio sources, video on demand platforms and streamingvideo and audio services platforms, one or more content data networks,data servers, web servers and other content servers, and/or othersources of media.

In various embodiments, the communications network 125 can includewired, optical and/or wireless links and the network elements 150, 152,154, 156, etc. can include service switching points, signal transferpoints, service control points, network gateways, media distributionhubs, servers, firewalls, routers, edge devices, switches and othernetwork nodes for routing and controlling communications traffic overwired, optical and wireless links as part of the Internet and otherpublic networks as well as one or more private networks, for managingsubscriber access, for billing and network management and for supportingother network functions.

FIG. 2A is a block diagram illustrating an example, non-limitingembodiment of a system 200 a functioning within, or operatively overlaidupon, the communication network 100 of FIG. 1 in accordance with variousaspects described herein. In particular, the system 200 a may include atower/base station 202 a that may be used to provide service to one ormore communication devices, e.g., communication devices 206 a, 210 a,214 a, 218 a, 222 a, and 226 a. The tower 202 a may be communicativelylinked/coupled to backhaul infrastructure (not shown in FIG. 2A) viawired and/or wireless connections.

The coverage provided by the tower 202 a may be divided into multiplesectors/faces, such as for example three sectors/faces denoted assector/face A, second/face B, and sector/face C in FIG. 2A. Each of thesectors/faces may be further divided into multiple cells, e.g., cell 234a in FIG. 2A. Each cell within a sector/face may operate at a distinctcarrier frequency. The use of multiple carrier frequencies within asector/face may enhance a data carrying capacity, which in turn mayenhance a quality of experience (QoE) or quality of service (QoS).

In the instance of the exemplary system 200 a shown in FIG. 2A, thecommunication devices 206 a and 210 a may obtain service via thesector/face A, the communication devices 214 a-222 a may obtain servicevia the sector/face B, and the communication device 226 a may obtainservice via the sector/face C. However, one or more of the communicationdevices 206 a-226 a may be a mobile device and may migrate from a scopeof coverage associated with a first sector/face (e.g., sector/face A) toa scope of coverage associated with a second sector/face (e.g.,sector/face B). In this regard, the tower 202 a may facilitate ahandover of service (e.g., a handover of a communication session) fromthe first sector/face to the second sector/face. Still further, in someembodiments a handover of service may be provided from the tower 202 ato another tower (not shown in FIG. 2A) if a communication device leavesthe range of coverage provided by any of the sectors/faces associatedwith the tower 202 a.

Aspects of the system 200 a may be implemented in conjunction with anallocation of resources. To demonstrate, and referring to FIG. 2B, asystem 200 b is shown that may be used to dimension/allocate resources(e.g., radio resources, communication bandwidth, control resources,etc.) associated with a communication network or system, such as thesystem 200 a of FIG. 2A. The system 200 b may include a load-awaredimensioning engine 204 b, a forecasting engine 208 b, and a QoS-awaredimensioning engine 212 b.

The load-aware dimensioning engine 204 b may generate profiles for,e.g., each cell of the network or system. The profiles, which mayinclude or be based on various parameters (e.g., signals, interference,noise, etc.), may be specified in an uplink direction, a downlinkdirection, or both uplink and downlink directions. In some embodiments,one or more of the parameters may be combined in connection with a givenprofile. For example, in some embodiments the load-aware dimensioningengine 204 b may generate a signal-to-interference-plus-noise (SINR)profile for a given cell. The SINR profile may be based at least in parton estimates/projections of one or more communication devices beinglocated within the cell, estimates/projections of one or morecommunication sessions of the communication device(s) falling within agiven SINR class/category, and estimates/projections and/or measurementsof throughput within the given SINR class/category. The typical range ofpossible spectral efficiencies that a random communication device mayexperience, with reference to a particular cell j, may be segmented intoa plurality of bins m_(j). The spectral efficiency in bin u withreference to cell j may be represented by the bin-specific spectralefficiency parameter θ_(ju). Aspects of past records (e.g., past drivetest records) may drive values for p_(ju), which denotes the probabilitythat a random communication device attached to cell j will find itselfin bin u.

The forecasting engine 208 b may generate forecasts of traffic in thenetwork or system. The forecasts may be based on traffic projections ata given level of granularity. In some embodiments, the generation of theforecasts may take into considerations of a type of traffic (e.g., voiceand video), and elasticity in terms of data volume at different prioritylevels/classes.

The QoS-aware dimensioning engine 212 b may be operative on the outputsof the load-aware dimensioning engine 204 b and the forecasting engine208 b to provide/generate dimensioned resource allocations. Thegeneration of such resource allocations by, e.g., the QoS-awaredimensioning engine 212 b is described in further detail below inconnection with, e.g., the method 200 c of FIG. 2C. Aspects of themethod 200 c may be executed/implemented in conjunction with, or withrespect to, an uplink direction, a downlink direction, or a combinationthereof.

For purposes of illustration, it may be assumed that a particularsector/face of a system (e.g., system 200 a of FIG. 2A) initially has atotal of Ψ carriers or cells, where each of the carriers/cells isarranged in a predetermined order of deployment, and each of thecarriers/cells is indexed as j=1, . . . , Ψ. Further, it may be assumedthat the cells corresponding to j=1 through j=Ψ−1 are at their highestsubscription level, and cell j=Ψ may be at any one of its intermediatesubscription levels (e.g., a subscription level that is less than, orequal to, a subscription capacity maximum for the cell). Still further,each carrier/cell j may initially have a provisioned spectrum Moreover,each carrier/cell j may have an associated spectral efficiency. Theaverage spectral efficiency for the j^(th) carrier/cell, denoted asθ_(j), may be calculated as follows:

θ_(j)=[Σ_(u)(p _(j,u)θ_(j,u))]⁻¹,

where p_(j,u) denotes the probability of a communication session of acommunication device falling within an SINR bin u of cell j for anarbitrary number of bins m (e.g., u=0, . . . , m−1), and θ_(j,u) denotesthe spectral efficiency in bin u ({p_(j,u), θ_(j,u)} are assumed to beavailable beforehand from drive test data).

In the description that follows, it is assumed that there are twoguaranteed traffic classes (corresponding to k=0 and k=1), where theguaranteed traffic classes correspond to: (1) conversational voice(k=0), e.g., voice over LTE protocol [VoLTE] supported at QCI prioritylevel 1 in LTE networks, and (2) conversational video (k=1), e.g., livestreaming supported at QCI priority level 2 in LTE networks. Stillfurther, it is assumed that there are four elastic data classes(corresponding to k=2 through k=5). The elastic data classes (k=2through k=5) each may correspond to/include any combination of bufferedvideo, email, text (documents, chat), file transfers, peer-to-peer filesharing, progressive video, and interactive gaming; e.g., the four datatraffic classes supported at QCI priority levels 6-9 in LTE networks.One skilled in the art would appreciate that these assumptions may berelaxed in a given embodiment to provide for more or less guaranteedtraffic classes and/or more or less elastic data classes.

With the foregoing assumptions in place, in block 202 c a cell capacityfor each of the Ψ cells, C_(j), j=1, . . . , Ψ, may be computed. Thecapacity of the j^(th) cell, C_(j), may be computed as the product ofthe spectrum configured for the cell (B_(j)) and the spectral efficiencyof the cell (θ_(j)), e.g.:

C _(j) =B _(j)*θ_(j)

In block 206 c, a determination may be made whether the trafficutilization associated with the guaranteed traffic classes (e.g.,conversational voice (k=0) and conversational video (k=1) in accordancewith the foregoing assumptions) exceeds a threshold R_(max), whereR_(max) represents the maximum fraction of the available capacityallowed for the guaranteed traffic. If the guaranteed trafficutilization does not exceed the threshold (R_(max)), flow may proceedfrom block 206 c to block 210; otherwise, flow may proceed from block206 c to block 230 c.

In block 210 c, erlangs associated with the guaranteed traffic classesmay be apportioned. For example, and in accordance withprojections/estimates regarding demand, E₀ may denote the erlangs forconversational voice (k=0) and E₁ may denote the erlangs forconversational video (k=1). As part of block 210 c, the elastic datavolume of the sector/face (denoted as EDV) may be apportioned to each ofthe cells j in an amount EDV_(j) that is in proportion to the capacityof the cell (C_(j)) relative to the total capacity (Σ_(q) C_(q)), e.g.:

EDV_(j)=EDV*(C _(j))/(Σ_(q) C_(q)),

where the summation operator (Σ) is applied for all cells ‘q’ from 1through Ψ. In some embodiments, the apportioning for E₀ and E₁ may usethe same, or a similar, scaling as set forth above for the elastic datavolume.

In block 214 c, the elastic data capacity (D_(j)) for elastic datatraffic (e.g., k=2 through k=5 in the foregoing description) for each ofthe j cells may be computed as the difference between the capacity ofthe cell (C_(j)) and the capacity allocated to the guaranteed trafficclasses (k=0 and k=1 in the foregoing description). In other words, theelastic data capacity may be computed as:

D _(j) =C _(j)−([E _(j,0) *b ₀]+[E _(j,1) *b ₁]),

where E_(j,0) denotes the apportioned erlangs for the j^(th) cellassociated with the first guaranteed traffic class (e.g., k=0), E_(j,1)denotes the erlangs for the j^(th) cell associated with the secondguaranteed traffic class (e.g., k=1), b₀ denotes the bandwidth occupiedby each session of the first guaranteed traffic class, and bi denotesthe bandwidth occupied by each session of the second guaranteed trafficclass.

In block 218 c, and as described in further detail below, analyticalmodeling and/or simulation may be performed based on the elastic datacapacity computed in block 214 c to determine/compute throughput T_(j,k)for each cell j and each of the elastic data classes (k=2 through k=5 inthis example).

The choice of whether to perform analytical modeling or simulation aspart of block 218 c may be based on one or more considerations. Anapportioning of traffic volumes among the cells (i.e., load balancing)may be common to both analytical modeling and simulation, and both maybe used to compute performance (e.g., throughput) in an individual cell(post load balancing). However, different tradeoffs may be presentbetween execution speed and granularity of results. For example,analytical modeling may be fast but might only provide an average ofthroughput estimates. Conversely, simulation may tend to be slow, butmay provide greater flexibility and may provide more refined metricssuch as a cumulative distribution function (CDF) of throughputs.

In block 222 c, a determination may be made whether the throughputscomputed in block 218 c all satisfy a threshold, denoted as S_(min)(k).For example, each of the classes (k=2 through k=5) may have its ownthreshold (e.g., S_(min,k)) as part of block 222 c.

If the determination of block 222 c is answered in the affirmative, flowmay proceed from block 222 c to block 226 c. Otherwise, flow may proceedfrom block 222 c to block 230 c.

In block 226 c, the count of cells may be updated to correspond to thelast count of cells. As described above, initially the count of cellsmay be set equal to Ψ; however, execution of the method 200 c (e.g.,block 234 c described below) may result in a count that is differentfrom Ψ. As part of block 226 c, the spectrum that is allocated to agiven cell (e.g., the cell corresponding to j=Ψ) may be updated tocorrespond to the last spectrum allocation to that cell. For example,execution of block 234 c or block 238 c described below may result in anew/different spectrum allocation.

In block 230 c, a determination may be made whether the last cell (e.g.,the cell corresponding to j=Ψ) is at a threshold (e.g., maximal)spectrum allocation. If so, flow may proceed from block 230 c to block234 c. Otherwise, flow may proceed from block 230 c to block 238 c.

In block 234 c, a new cell (e.g., a cell corresponding to j=Ψ+1) may bedeployed at a predetermined (e.g., a minimum, discrete) spectrum level.From block 234 c, flow may proceed to block 202 c to facilitateadditional (e.g., continued or periodic) executions of the method 200 c.

In block 238 c, the spectrum associated with the last cell (e.g., j=Ψ)may be increased (e.g., incremented) to the next available level. Fromblock 238 c, flow may proceed to block 202 c to facilitate additional(e.g., continued or periodic) executions of the method 200 c.

As described above in relation to block 218 c, in some embodimentsanalytical modeling may be utilized to compute the throughput (T_(j,k))for each elastic data class (k=2 through k=5 in the given example) foreach cell (initially, j=1 through j=Ψ). For a given cell j, theanalytical modeling may be based on the elastic data volume apportionedto the cell (EDV_(j)) as computed/determined in block 210 c and theelastic data capacity (D_(j)) computed/determined in block 214 c. Inparticular, and with reference to G. Fayolle et al., “Sharing aProcessor Among Many Job Classes”, Journal of the ACM, Vol. 27, No. 3,July 1980, pp. 519-532, the contents of which are incorporated herein byway of reference, equations described therein (hereinafter referred toas the Fayolle equations) may be utilized/solved to compute thethroughputs (T_(j,k)) as part of block 218 c. To demonstrate, and forthe elastic data classes (k=2 through k=5 in this example), and assuminga set of scheduler priority weights wk for k=2 through k=5, thefollowing system of Fayolle equations may be computed/solved toultimately obtain the throughputs (T_(j,k)):

x _(j,k)*[1−Σ_(r)((Q _(j,r) *w _(r))/(w _(r) +w _(k)))]−[Σ_(r)((Q _(j,r)*w _(r))*x _(j,r)/(w _(r) +w _(k)))=1/D _(j),

where Q_(j,r)=EDV_(j,r)/D_(j) and denotes the class ‘r’ resource blockutilization within cell j, and the summation operators (Σ_(r)) areapplied over the total number of elastic data priority classes (e.g.,r=2 through r=5 in this example). In particular, the Fayolle equationsmay be solved to determine values for the variables x_(j,k). Once thevalues x_(j,k) are known/computed, the throughputs (T_(j,k)) may becomputed as the inverse of those values, e.g.:

T _(j,k)=1/x _(j,k)

As described above in relation to block 218 c, in some embodimentssimulation may be utilized to compute the throughput (T_(j,k)) for eachelastic data class (k=2 through k=5 in the given example) for each cell(initially, j=1 through j=Ψ). Within the simulation, a Markovianbirth-death queuing framework may be adopted/incorporated. Each of theguaranteed traffic classes (k=0 and k=1 in the foregoing description)may have an exponentially distributed random holding time with a fixedaverage denoted as H_(k); e.g., for the first guaranteed traffic classthe average holding time may be assumed to be H₀ and for the secondguaranteed traffic class the average holding time may be assumed to beH₁. As used herein, a holding time may be assumed to be approximatelyequal to an amount of time that a resource is utilized/consumed as partof a communication session associated with a given traffic class. Eachof the elastic data classes (e.g., k=2 through k=5 in the foregoingdescription) may have associated transactions, where each transactionincludes an exponentially distributed average payload pld_(k).Inter-arrival times of guaranteed traffic class transactions (i.e.,voice/video calls) and elastic data traffic class transactions may bemodeled as exponentially distributed random variables. Time-based epochsmay be defined by one or more events, such as for example an arrival(e.g., a start) of a session associated with a guaranteed traffic class(e.g., k=0 and k=1 in the foregoing examples), an arrival of a sessionassociated with an elastic data class (e.g., k=2 through k=5 in theforegoing examples), a departure (e.g., a termination) of a sessionassociated with a guaranteed traffic class, and a departure of a sessionassociated with an elastic data class.

With the foregoing assumptions in place as part of the simulation, astate of the system or network may be captured in a vector of the formn_(k,u), where n_(k,u) denotes the number of active sessions belongingto QoS/priority class k and SINR bin u (for u=0 to u=m−1). Uponarrival/start of a session, the corresponding SINR class (u) may bechosen at random per a probability mass distribution {p_(u)} (estimatedbeforehand from drive test data).

Referring now to the method 200 d of FIG. 2D, in block 204 dparameters/variables of the method may be initialized. For example, aspart of block 204 d, the state vector (n_(k,u)) may be initialized tozero and a simulation time (sim_(t)) may be set equal to zero.Furthermore, the spectrum share available for elastic data, BD, is setequal to B, the total spectrum configured at the cell.

In block 208 d, a sojourn time interval (τ) may be computed. The sojourntime interval (τ) may be modeled as an exponentially distributed randomvariable, in accordance with a state exit rate that is equal to the sumof arrival and departure rates (known whenever this block is entered) asdescribed in further detail below. At the start of simulation (orfollowing an epoch when the system becomes empty) there are no pendingtransactions (i.e., n_(k,u)=0∀k, u); hence the net departure rate is 0.The exponentially distributed sojourn time τ in this situation isdetermined in block 204 d as the sum of only the (known) arrival ratesfrom all traffic classes. Transaction arrival rates for voice and video(k=0, 1) equal the respective projected Erlangs divided by therespective holding times, and those for each of the elastic data classes(k=2, . . . , 5) equals the respective projected traffic volume dividedby an assumed payload size per transaction x (a specified systemconstant, e.g., x=1 Mbits). In the more general situation where thereare pending transactions, computation of the non-zero departure rates tobe included is state-dependent, hence more complex, and will bedescribed in further detail in the sequel.

In block 212 d, the simulation time (sim_(t)) may be set equal to thesum of the current or last value of the simulation time (sim_(t)) andthe sojourn interval (τ)−advancement of the simulated virtual time.

In block 216 d, a specific coarse event (i.e., an arrival or adeparture) may be differentiated, via pseudo-random coin toss based onthe logic that the probability of that event (arrival/departure) equalsthe exit rate into that event (from the preceding state) divided by thetotal exit rate from the preceding state. For example, at the top levelof hierarchy, the average sojourn time equals 1/(arrival rate+departurerate), prob(next event being arrival)=arrival rate/(arrivalrate+departure rate) and prob(next event being departure)=departurerate/(arrival rate+departure rate). If the event is an arrivalassociated with a guaranteed traffic class, flow may proceed from block216 d to block 220 d. If the event is an arrival associated with anelastic data traffic class, flow may proceed from block 216 d to block224 d. If the event is a departure associated with a guaranteed trafficclass, flow may proceed from block 216 d to block 228 d. If the event isa departure associated with an elastic traffic class, flow may proceedfrom block 216 d to block 232 d. Note that finer-grain differentiationamong guaranteed or elastic arrivals and guaranteed or elastic classdepartures is again carried out based on pseudo-random coin tossesdriven by ratios of the (known) entrance rates, analogously as describedabove for coarse event differentiation.

In block 220 d, a determination may be made whether call blocking isimplemented (either broadly speaking, or with respect to the particulararrival identified in block 216 d. Blocking may entail effectivelydiscarding/ignoring/dropping the arrival (as identified in block 216 d)if it meets one of a set of blocking criteria—e.g., if the current totalvoice and video occupancy in the system exceeds an allowed threshold, orby virtue of a junk/spam filter that blocks incoming arrivals fromparticular phone numbers or communication devices. If the incomingarrival is blocked, flow may proceed from block 220 d to block 208 d.Otherwise the arrival is accepted, and flow may proceed from block 220 dto block 236 d.

In block 236 d, the state vector (n_(k,u), k=0, 1) may be incremented inaccordance with the guaranteed traffic class arrival (of block 216 d),thereby increasing the resource/capacity utilization for guaranteedtraffic and effectively decreasing the resources/capacity available forelastic data. Fine grain identification of a specific guaranteed classamong k=0, 1, is carried out based on the pseudo-random coin toss logicdriven by ratios of exit rates, described above for event classificationat the higher levels of hierarchy. Next, fine grain identification of aspecific SINR bin u is carried out via pseudo-random coin toss logicdriven by the known probabilities of a transaction falling in variousSINR bins (i.e., {p_(k,u)}). The aforementioned logic is analogouslyapplicable to the description set forth below as well. To enablecomputation of the next sojourn time τ in block 216 d and the followingevent resolutions as described above, the departure rate from thecurrent state is now updated by adding the inverse of the call holdingtime. Furthermore, the spectrum budget available to data BD is decreasedby b_(k)/θ_(.,u) where b_(k) denotes the payload bandwidth occupied bythe arriving guaranteed transaction (of priority class k=0, 1) andθ_(.,u) denotes the spectral efficiency achieved in the SINR bin u forthe cell in consideration, where the arrival landed.

In block 224 d, the state occupancy (n_(k,u), k=2, . . . , 5) may beincremented in accordance with the elastic data traffic class arrival(of block 216 d). User throughputs as well as exit rates for all datastates (i.e., {(k, u), k=2, . . . , 5} may need to be updatedconcurrently in block 224 d, since (unlike in the case of guaranteedtransactions), they are interrelated for elastic data transactions. Thecomplex procedure for this step is detailed further below.

In block 228 d, the state occupancy (n_(k,u), k=0, 1) may be decrementedin accordance with the guaranteed traffic class departure (of block 216d), thereby decreasing the resource/capacity utilization for guaranteedtraffic and effectively increasing the resources/capacity available forelastic data. In particular, the spectrum budget available to data BD isdecreased by b_(k)/θ_(.,u) where b_(k) denotes the payload bandwidthoccupied by the departing guaranteed transaction (of priority classk=0, 1) and θ_(.,u) denotes the spectral efficiency achieved in the SINRbin u for the cell in question, where the departing transactionbelonged. Also, the departure rate from this state is updated bysubtracting the inverse of the call holding time.

In block 232 d, the state occupancy (n_(k,u), k=2, . . . , 5) may bedecremented in accordance with the elastic data traffic class departure(of block 216 d). User throughputs as well as exit rates for allcomponents of the state vector for data (i.e., {n_(k,u), k=2, . . . , 5}may need to be updated concurrently in block 232 d, since (unlike in thecase of guaranteed transactions), they are interrelated for elastic datatransactions. The complex procedure for this step is detailed furtherbelow.

As part of each of blocks 236 d, 224 d, 228 d, and 232 d, guaranteedtraffic class and elastic data traffic class session arrival ordeparture rates may be updated/captured, as applicable, based on thenature of the event identified in block 216 d. For example, for a givenclass of the guaranteed traffic classes, session arrival rates (seeblock 236 d) may be computed as the erlangs for the given class dividedby the holding time for the class. For a given class of the guaranteedtraffic classes, session departure rates (see block 228 d) may becomputed as the number of active sessions for the given class divided bythe hold time for the class. Following the arrival/departure of aguaranteed class transaction, the spectrum share available for elasticdata, BD, may be decreased/increased by an amount equal to the bandwidthoccupancy of the transaction divided by the spectrum efficiency enjoyedby the transaction. For a given class of the elastic data trafficclasses, session arrival rates (see block 224 d) may be computed as therespective volume of data per unit time (as illustratively measured inMegabits per second [Mbps]) divided by the data payload size of thesession/transaction x (e.g., x=1 Mbits). Departure rates for sessions ofthe elastic data traffic classes (see block 232 d) may be interrelatedto each other and to the number of transactions associated with theguaranteed traffic classes, and may be modeled/computed in accordancewith scheduling priority rules.

To determine the update rules applicable for each elastic data classstate (k, u), with , k=2, . . . , 5 being the traffic class and u beingthe SINR bin, the procedure may be as follows. Per the weightedproportionate-fairness scheduling policy assumed to be in place withinthe cell schedulers, the current spectrum share of each activetransaction in this state may be given by s_(k)=BD×w_(k)/(Σ_(l) Σ_(m)w_(l)n_(l,m)), the summation in the denominator being across all datastates (i.e., l=2, . . . , 5), where BD denotes the currently availableaggregate data spectrum share, w_(l) denotes the scheduler weightassigned to elastic priority class l and n_(l,m) denotes the number ofcurrently ongoing transactions in elastic state (l, m). Next, thecurrent user throughput enjoyed by each active transaction in state (k,u) is given by T_(k,u)=s_(k)×θ_(.,u) where θ_(.,u) denotes the spectrumefficiency achievable in SINR bin u for the cell in question, asrecorded in advance from drive test data. Finally, the departure ratefrom state (k, u) is given by n_(k,u)×T_(k,u)/x. Note that these stepsmay need to be carried out for all states upon each arrival to/departurefrom any of the elastic data or guaranteed states. The procedure may besimpler when a guaranteed transaction arrival/departure occurs—only BDmay need to be scaled in the above equations.

As part of each of blocks 236 d, 224 d, 228 d, and 232 d, aggregatestate exit rates may be computed to facilitate the sojourn timecomputation and event resolution (based on pseudo-random tosses aided byratios of rates). For example, the (total) state exit rate may becomputed as the sum of all session/transaction arrival rates and thesession/transaction departure rates described above. The state exit ratemay be used to determine the sojourn time interval (τ) as describedabove in connection with block 208 d.

As part of each of blocks 236 d, 224 d, 228 d, and 232 d, samples ofthroughput (T_(k,u)) may be recorded for a communication device for eachk and u, where applicable, with a statistical probability weightingapplied thereto. In some embodiments, the probability weighting may bebased on (a product of) the state vector (n_(k,u)) and the sojourn timeinterval (τ).

In block 240 d, a determination may be made whether the simulation time(sim_(t)) exceeds a threshold (time_limit). If not, flow may proceedfrom block 240 d to block 208 d; otherwise, flow may proceed from block240 d to block 244 d. The threshold (time_limit) of block 240 d may bebased on experimentation and may be selected to ensureaccuracy/convergence is obtained, while at the same time avoidingexcessive delay (e.g., delay in an amount greater than a threshold) ingenerating simulation outputs/results described below.

In block 244 d, the (probability weighted) recorded throughputvalues/samples obtained as part of blocks 236 d, 224 d, 228 d, and 232 dmay be processed, potentially as part of generating one or more outputs(e.g., reports, displays, audio renderings/presentations, etc.). Theprocessing of block 244 d may include the application of one or morefilters to reduce the impact of spurious values/samples.

In block 248 d, a blocking probability for guaranteed traffic classesmay be computed and/or a tail probability for the data (e.g., therecorded throughput values, as subject to any probability weighting) maybe computed. The computations of block 248 d may be used as part ofadditional/future executions of the method 200 c and/or the method 200 d(e.g., may be used as a filter or prediction of a likelihood of anevent—e.g., a blocking—occurring, which may be used to modify resourceallocations potentially as part of one or more weightings). Thecomputations of block 248 d may provide an indication of a degree ofconfidence in the outputs of block 244 d, which may serve as a furtherrefinement in relation to resource allocations.

In some embodiments, the determination of the event type in block 216 dmay adhere/conform to three hierarchical steps. In a first of the steps,a coarse resolution may be performed to identify the event as among oneof: (a) an arrival, (b) a first guaranteed traffic class departure, (c)a second guaranteed traffic class departure, and (d) an elastic datatraffic class departure. In a second of the steps, if the event is: anarrival (a), then a one-step array lookup may be used to resolve its QoSand SINR class (i.e., the computational steps of block 216 d may bereplaced by an associative array lookup at fine granularity, to speed upexecution); if it is a departure involving a guaranteed traffic class((b) or (c)), then a resolution of the SINR class may be performed; ifit is an elastic data traffic class departure (d), then a resolution ofthe QoS class may be performed. In a third of the steps, if the event isa departure involving a certain QoS class (as determined in the secondstep) of an elastic data traffic class (as determined in the firststep), then a resolution of the SINR class may be performed.

While for purposes of simplicity of explanation, the respectiveprocesses are shown and described as a series of blocks in FIGS. 2C and2D, it is to be understood and appreciated that the claimed subjectmatter is not limited by the order of the blocks, as some blocks mayoccur in different orders and/or concurrently with other blocks fromwhat is depicted and described herein. Moreover, not all illustratedblocks may be required to implement the methods described herein.

According to aspects of this disclosure, a QoS-based approach may beutilized as part of dimensioning/allocating resources (e.g., wirelessspectrum) for a network. Such allocations may be based on a progressivesearch/query for the required number of cells and cell bandwidthassignments that may meet/satisfy stability and target performancecriteria. In various embodiments, a sector/face load may be split amongmultiple cells in accordance with load-balancing policies. A schedulingpolicy may allocate resources in accordance with weights assigned toelastic data traffic classes.

Aspects of this disclosure provide analytical modeling and simulation astechniques for estimating performance in conjunction with resourceallocations. Analytical modeling may enable dimensioning/allocationsbased on average throughput criteria. Simulation may be used to enabledimensioning/allocations based on more refined tail probabilitythroughput criteria.

As described herein, aspects of this disclosure provide for resource(e.g., wireless spectrum) dimensioning/allocations subject to meetingdifferentiated QoS targets among multiple types of traffic classes. Asset forth herein, VoIP, (conversational) video, and elastic data areexamples of QoS traffic classes that may be used. Other forms/types oftraffic classifications may be included/utilized in some embodiments.

As set forth above, additional carriers/cells may be allocated within agiven sector/face in response to changes (e.g., increases) in demand.Accordingly, aspects of this disclosure may reduce (e.g., minimize)inter-sector interference by only using/allocating carriers/cells thatare needed to meet QoS demands/requirements.

Aspects of this disclosure may incorporate SINR profiles, traffic demandmeasurements and estimates, performance criteria, and scheduling andload balancing policies as part of generating resource (e.g., spectrum,carrier frequency, bandwidth, etc.) allocations in a network.

Referring now to FIG. 3, a block diagram 300 is shown illustrating anexample, non-limiting embodiment of a virtualized communication networkin accordance with various aspects described herein. In particular avirtualized communication network is presented that can be used toimplement some or all of the subsystems and functions of system 100, thesubsystems and functions of systems 200 a and 200 b, and methods 200 cand 200 d presented in FIG. 1 and FIGS. 2A-2D. For example, virtualizedcommunication network 300 can facilitate in whole or in part computing acapacity for each cell of a plurality of cells associated with anetwork, responsive to determining that a utilization of wirelessspectrum associated with a first plurality of classes of traffic in thenetwork is greater than a first threshold, upgrading a capacity in thenetwork, and responsive to determining that the utilization of thewireless spectrum associated with the first plurality of classes oftraffic in the network is less than or equal to the first threshold:computing a capacity for a second plurality of classes of traffic foreach cell of the plurality of cells in accordance with the capacity foreach cell, performing analytical modeling or engaging a simulation todetermine a throughput for each of the second plurality of classes oftraffic for each cell of the plurality of cells in accordance with thecomputing of the capacity for the second plurality of classes oftraffic, and responsive to determining that the throughput for at leastone of the second plurality of classes of traffic for at least one cellof the plurality of cells is less than a second threshold, upgrading thecapacity in the network, wherein the upgrading of the capacity in thenetwork comprises one of: deploying a new cell at a predetermined levelof wireless spectrum, wherein the new cell is not included in theplurality of cells, or increasing a wireless spectrum allocation of afirst cell of the plurality of cells. Virtualized communication network300 can facilitate in whole or in part responsive to determining that autilization of wireless spectrum associated with a first class oftraffic in a network is greater than a first threshold, performing anupgrade of a capacity in the network, and responsive to determining thatthe utilization of the wireless spectrum associated with the first classof traffic in the network is less than or equal to the first threshold:computing a capacity for a second class of traffic for each cell of aplurality of cells of the network, performing analytical modeling orengaging a simulation to determine a throughput for the second class oftraffic for each cell of the plurality of cells in accordance with thecomputing of the capacity for the second class of traffic, andresponsive to determining that the throughput for the second class oftraffic for at least one cell of the plurality of cells is less than asecond threshold, performing the upgrade of the capacity in the network,wherein the performing of the upgrade of the capacity in the networkcomprises one of: deploying a new cell at a predetermined level ofwireless spectrum, wherein the new cell is not included in the pluralityof cells, or increasing a wireless spectrum allocation of a first cellof the plurality of cells. Virtualized communication network 300 canfacilitate in whole or in part determining whether a utilization ofwireless spectrum associated with a guaranteed class of traffic in anetwork is greater than a first threshold, responsive to the determiningindicating that the utilization of the wireless spectrum associated withthe guaranteed class of traffic is greater than the first threshold,causing an upgrade of a capacity in the network, and responsive to thedetermining indicating that the utilization of the wireless spectrumassociated with the guaranteed class of traffic is not greater than thefirst threshold: determining a throughput for a non-guaranteed class oftraffic for each cell of a plurality of cells of the network, andresponsive to determining that the throughput for the non-guaranteedclass of traffic for at least one cell of the plurality of cells is lessthan a second threshold, causing the upgrade of the capacity in thenetwork, wherein the causing of the upgrade of the capacity in thenetwork comprises: deploying a new cell at a predetermined level ofwireless spectrum, wherein the new cell is not included in the pluralityof cells, increasing a wireless spectrum allocation of a first cell ofthe plurality of cells, or a combination thereof.

In particular, a cloud networking architecture is shown that leveragescloud technologies and supports rapid innovation and scalability via atransport layer 350, a virtualized network function cloud 325 and/or oneor more cloud computing environments 375. In various embodiments, thiscloud networking architecture is an open architecture that leveragesapplication programming interfaces (APIs); reduces complexity fromservices and operations; supports more nimble business models; andrapidly and seamlessly scales to meet evolving customer requirementsincluding traffic growth, diversity of traffic types, and diversity ofperformance and reliability expectations.

In contrast to traditional network elements—which are typicallyintegrated to perform a single function, the virtualized communicationnetwork employs virtual network elements (VNEs) 330, 332, 334, etc. thatperform some or all of the functions of network elements 150, 152, 154,156, etc. For example, the network architecture can provide a substrateof networking capability, often called Network Function VirtualizationInfrastructure (NFVI) or simply infrastructure that is capable of beingdirected with software and Software Defined Networking (SDN) protocolsto perform a broad variety of network functions and services. Thisinfrastructure can include several types of substrates. The most typicaltype of substrate being servers that support Network FunctionVirtualization (NFV), followed by packet forwarding capabilities basedon generic computing resources, with specialized network technologiesbrought to bear when general purpose processors or general purposeintegrated circuit devices offered by merchants (referred to herein asmerchant silicon) are not appropriate. In this case, communicationservices can be implemented as cloud-centric workloads.

As an example, a traditional network element 150 (shown in FIG. 1), suchas an edge router can be implemented via a VNE 330 composed of NFVsoftware modules, merchant silicon, and associated controllers. Thesoftware can be written so that increasing workload consumes incrementalresources from a common resource pool, and moreover so that it'selastic: so the resources are only consumed when needed. In a similarfashion, other network elements such as other routers, switches, edgecaches, and middle-boxes are instantiated from the common resource pool.Such sharing of infrastructure across a broad set of uses makes planningand growing infrastructure easier to manage.

In an embodiment, the transport layer 350 includes fiber, cable, wiredand/or wireless transport elements, network elements and interfaces toprovide broadband access 110, wireless access 120, voice access 130,media access 140 and/or access to content sources 175 for distributionof content to any or all of the access technologies. In particular, insome cases a network element needs to be positioned at a specific place,and this allows for less sharing of common infrastructure. Other times,the network elements have specific physical layer adapters that cannotbe abstracted or virtualized, and might require special DSP code andanalog front-ends (AFEs) that do not lend themselves to implementationas VNEs 330, 332 or 334. These network elements can be included intransport layer 350.

The virtualized network function cloud 325 interfaces with the transportlayer 350 to provide the VNEs 330, 332, 334, etc. to provide specificNFVs. In particular, the virtualized network function cloud 325leverages cloud operations, applications, and architectures to supportnetworking workloads. The virtualized network elements 330, 332 and 334can employ network function software that provides either a one-for-onemapping of traditional network element function or alternately somecombination of network functions designed for cloud computing. Forexample, VNEs 330, 332 and 334 can include route reflectors, domain namesystem (DNS) servers, and dynamic host configuration protocol (DHCP)servers, system architecture evolution (SAE) and/or mobility managemententity (MME) gateways, broadband network gateways, IP edge routers forIP-VPN, Ethernet and other services, load balancers, distributers andother network elements. Because these elements don't typically need toforward large amounts of traffic, their workload can be distributedacross a number of servers—each of which adds a portion of thecapability, and overall which creates an elastic function with higheravailability than its former monolithic version. These virtual networkelements 330, 332, 334, etc. can be instantiated and managed using anorchestration approach similar to those used in cloud compute services.

The cloud computing environments 375 can interface with the virtualizednetwork function cloud 325 via APIs that expose functional capabilitiesof the VNEs 330, 332, 334, etc. to provide the flexible and expandedcapabilities to the virtualized network function cloud 325. Inparticular, network workloads may have applications distributed acrossthe virtualized network function cloud 325 and cloud computingenvironment 375 and in the commercial cloud, or might simply orchestrateworkloads supported entirely in NFV infrastructure from these thirdparty locations.

Turning now to FIG. 4, there is illustrated a block diagram of acomputing environment in accordance with various aspects describedherein. In order to provide additional context for various embodimentsof the embodiments described herein, FIG. 4 and the following discussionare intended to provide a brief, general description of a suitablecomputing environment 400 in which the various embodiments of thesubject disclosure can be implemented. In particular, computingenvironment 400 can be used in the implementation of network elements150, 152, 154, 156, access terminal 112, base station or access point122, switching device 132, media terminal 142, and/or VNEs 330, 332,334, etc. Each of these devices can be implemented viacomputer-executable instructions that can run on one or more computers,and/or in combination with other program modules and/or as a combinationof hardware and software. For example, computing environment 400 canfacilitate in whole or in part computing a capacity for each cell of aplurality of cells associated with a network, responsive to determiningthat a utilization of wireless spectrum associated with a firstplurality of classes of traffic in the network is greater than a firstthreshold, upgrading a capacity in the network, and responsive todetermining that the utilization of the wireless spectrum associatedwith the first plurality of classes of traffic in the network is lessthan or equal to the first threshold: computing a capacity for a secondplurality of classes of traffic for each cell of the plurality of cellsin accordance with the capacity for each cell, performing analyticalmodeling or engaging a simulation to determine a throughput for each ofthe second plurality of classes of traffic for each cell of theplurality of cells in accordance with the computing of the capacity forthe second plurality of classes of traffic, and responsive todetermining that the throughput for at least one of the second pluralityof classes of traffic for at least one cell of the plurality of cells isless than a second threshold, upgrading the capacity in the network,wherein the upgrading of the capacity in the network comprises one of:deploying a new cell at a predetermined level of wireless spectrum,wherein the new cell is not included in the plurality of cells, orincreasing a wireless spectrum allocation of a first cell of theplurality of cells. Computing environment 400 can facilitate in whole orin part responsive to determining that a utilization of wirelessspectrum associated with a first class of traffic in a network isgreater than a first threshold, performing an upgrade of a capacity inthe network, and responsive to determining that the utilization of thewireless spectrum associated with the first class of traffic in thenetwork is less than or equal to the first threshold: computing acapacity for a second class of traffic for each cell of a plurality ofcells of the network, performing analytical modeling or engaging asimulation to determine a throughput for the second class of traffic foreach cell of the plurality of cells in accordance with the computing ofthe capacity for the second class of traffic, and responsive todetermining that the throughput for the second class of traffic for atleast one cell of the plurality of cells is less than a secondthreshold, performing the upgrade of the capacity in the network,wherein the performing of the upgrade of the capacity in the networkcomprises one of: deploying a new cell at a predetermined level ofwireless spectrum, wherein the new cell is not included in the pluralityof cells, or increasing a wireless spectrum allocation of a first cellof the plurality of cells. Computing environment 400 can facilitate inwhole or in part determining whether a utilization of wireless spectrumassociated with a guaranteed class of traffic in a network is greaterthan a first threshold, responsive to the determining indicating thatthe utilization of the wireless spectrum associated with the guaranteedclass of traffic is greater than the first threshold, causing an upgradeof a capacity in the network, and responsive to the determiningindicating that the utilization of the wireless spectrum associated withthe guaranteed class of traffic is not greater than the first threshold:determining a throughput for a non-guaranteed class of traffic for eachcell of a plurality of cells of the network, and responsive todetermining that the throughput for the non-guaranteed class of trafficfor at least one cell of the plurality of cells is less than a secondthreshold, causing the upgrade of the capacity in the network, whereinthe causing of the upgrade of the capacity in the network comprises:deploying a new cell at a predetermined level of wireless spectrum,wherein the new cell is not included in the plurality of cells,increasing a wireless spectrum allocation of a first cell of theplurality of cells, or a combination thereof.

Generally, program modules comprise routines, programs, components, datastructures, etc., that perform particular tasks or implement particularabstract data types. Moreover, those skilled in the art will appreciatethat the methods can be practiced with other computer systemconfigurations, comprising single-processor or multiprocessor computersystems, minicomputers, mainframe computers, as well as personalcomputers, hand-held computing devices, microprocessor-based orprogrammable consumer electronics, and the like, each of which can beoperatively coupled to one or more associated devices.

As used herein, a processing circuit includes one or more processors aswell as other application specific circuits such as an applicationspecific integrated circuit, digital logic circuit, state machine,programmable gate array or other circuit that processes input signals ordata and that produces output signals or data in response thereto. Itshould be noted that while any functions and features described hereinin association with the operation of a processor could likewise beperformed by a processing circuit.

The illustrated embodiments of the embodiments herein can be alsopracticed in distributed computing environments where certain tasks areperformed by remote processing devices that are linked through acommunications network. In a distributed computing environment, programmodules can be located in both local and remote memory storage devices.

Computing devices typically comprise a variety of media, which cancomprise computer-readable storage media and/or communications media,which two terms are used herein differently from one another as follows.Computer-readable storage media can be any available storage media thatcan be accessed by the computer and comprises both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer-readable storage media can be implementedin connection with any method or technology for storage of informationsuch as computer-readable instructions, program modules, structured dataor unstructured data.

Computer-readable storage media can comprise, but are not limited to,random access memory (RAM), read only memory (ROM), electricallyerasable programmable read only memory (EEPROM),flash memory or othermemory technology, compact disk read only memory (CD-ROM), digitalversatile disk (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devicesor other tangible and/or non-transitory media which can be used to storedesired information. In this regard, the terms “tangible” or“non-transitory” herein as applied to storage, memory orcomputer-readable media, are to be understood to exclude onlypropagating transitory signals per se as modifiers and do not relinquishrights to all standard storage, memory or computer-readable media thatare not only propagating transitory signals per se.

Computer-readable storage media can be accessed by one or more local orremote computing devices, e.g., via access requests, queries or otherdata retrieval protocols, for a variety of operations with respect tothe information stored by the medium.

Communications media typically embody computer-readable instructions,data structures, program modules or other structured or unstructureddata in a data signal such as a modulated data signal, e.g., a carrierwave or other transport mechanism, and comprises any informationdelivery or transport media. The term “modulated data signal” or signalsrefers to a signal that has one or more of its characteristics set orchanged in such a manner as to encode information in one or moresignals. By way of example, and not limitation, communication mediacomprise wired media, such as a wired network or direct-wiredconnection, and wireless media such as acoustic, RF, infrared and otherwireless media.

With reference again to FIG. 4, the example environment can comprise acomputer 402, the computer 402 comprising a processing unit 404, asystem memory 406 and a system bus 408. The system bus 408 couplessystem components including, but not limited to, the system memory 406to the processing unit 404. The processing unit 404 can be any ofvarious commercially available processors. Dual microprocessors andother multiprocessor architectures can also be employed as theprocessing unit 404.

The system bus 408 can be any of several types of bus structure that canfurther interconnect to a memory bus (with or without a memorycontroller), a peripheral bus, and a local bus using any of a variety ofcommercially available bus architectures. The system memory 406comprises ROM 410 and RAM 412. A basic input/output system (BIOS) can bestored in a non-volatile memory such as ROM, erasable programmable readonly memory (EPROM), EEPROM, which BIOS contains the basic routines thathelp to transfer information between elements within the computer 402,such as during startup. The RAM 412 can also comprise a high-speed RAMsuch as static RAM for caching data.

The computer 402 further comprises an internal hard disk drive (HDD) 414(e.g., EIDE, SATA), which internal HDD 414 can also be configured forexternal use in a suitable chassis (not shown), a magnetic floppy diskdrive (FDD) 416, (e.g., to read from or write to a removable diskette418) and an optical disk drive 420, (e.g., reading a CD-ROM disk 422 or,to read from or write to other high capacity optical media such as theDVD). The HDD 414, magnetic FDD 416 and optical disk drive 420 can beconnected to the system bus 408 by a hard disk drive interface 424, amagnetic disk drive interface 426 and an optical drive interface 428,respectively. The hard disk drive interface 424 for external driveimplementations comprises at least one or both of Universal Serial Bus(USB) and Institute of Electrical and Electronics Engineers (IEEE) 1394interface technologies. Other external drive connection technologies arewithin contemplation of the embodiments described herein.

The drives and their associated computer-readable storage media providenonvolatile storage of data, data structures, computer-executableinstructions, and so forth. For the computer 402, the drives and storagemedia accommodate the storage of any data in a suitable digital format.Although the description of computer-readable storage media above refersto a hard disk drive (HDD), a removable magnetic diskette, and aremovable optical media such as a CD or DVD, it should be appreciated bythose skilled in the art that other types of storage media which arereadable by a computer, such as zip drives, magnetic cassettes, flashmemory cards, cartridges, and the like, can also be used in the exampleoperating environment, and further, that any such storage media cancontain computer-executable instructions for performing the methodsdescribed herein.

A number of program modules can be stored in the drives and RAM 412,comprising an operating system 430, one or more application programs432, other program modules 434 and program data 436. All or portions ofthe operating system, applications, modules, and/or data can also becached in the RAM 412. The systems and methods described herein can beimplemented utilizing various commercially available operating systemsor combinations of operating systems.

A user can enter commands and information into the computer 402 throughone or more wired/wireless input devices, e.g., a keyboard 438 and apointing device, such as a mouse 440. Other input devices (not shown)can comprise a microphone, an infrared (IR) remote control, a joystick,a game pad, a stylus pen, touch screen or the like. These and otherinput devices are often connected to the processing unit 404 through aninput device interface 442 that can be coupled to the system bus 408,but can be connected by other interfaces, such as a parallel port, anIEEE 1394 serial port, a game port, a universal serial bus (USB) port,an IR interface, etc.

A monitor 444 or other type of display device can be also connected tothe system bus 408 via an interface, such as a video adapter 446. Itwill also be appreciated that in alternative embodiments, a monitor 444can also be any display device (e.g., another computer having a display,a smart phone, a tablet computer, etc.) for receiving displayinformation associated with computer 402 via any communication means,including via the Internet and cloud-based networks. In addition to themonitor 444, a computer typically comprises other peripheral outputdevices (not shown), such as speakers, printers, etc.

The computer 402 can operate in a networked environment using logicalconnections via wired and/or wireless communications to one or moreremote computers, such as a remote computer(s) 448. The remotecomputer(s) 448 can be a workstation, a server computer, a router, apersonal computer, portable computer, microprocessor-based entertainmentappliance, a peer device or other common network node, and typicallycomprises many or all of the elements described relative to the computer402, although, for purposes of brevity, only a remote memory/storagedevice 450 is illustrated. The logical connections depicted comprisewired/wireless connectivity to a local area network (LAN) 452 and/orlarger networks, e.g., a wide area network (WAN) 454. Such LAN and WANnetworking environments are commonplace in offices and companies, andfacilitate enterprise-wide computer networks, such as intranets, all ofwhich can connect to a global communications network, e.g., theInternet.

When used in a LAN networking environment, the computer 402 can beconnected to the LAN 452 through a wired and/or wireless communicationnetwork interface or adapter 456. The adapter 456 can facilitate wiredor wireless communication to the LAN 452, which can also comprise awireless AP disposed thereon for communicating with the adapter 456.

When used in a WAN networking environment, the computer 402 can comprisea modem 458 or can be connected to a communications server on the WAN454 or has other means for establishing communications over the WAN 454,such as by way of the Internet. The modem 458, which can be internal orexternal and a wired or wireless device, can be connected to the systembus 408 via the input device interface 442. In a networked environment,program modules depicted relative to the computer 402 or portionsthereof, can be stored in the remote memory/storage device 450. It willbe appreciated that the network connections shown are example and othermeans of establishing a communications link between the computers can beused.

The computer 402 can be operable to communicate with any wirelessdevices or entities operatively disposed in wireless communication,e.g., a printer, scanner, desktop and/or portable computer, portabledata assistant, communications satellite, any piece of equipment orlocation associated with a wirelessly detectable tag (e.g., a kiosk,news stand, restroom), and telephone. This can comprise WirelessFidelity (Wi-Fi) and BLUETOOTH® wireless technologies. Thus, thecommunication can be a predefined structure as with a conventionalnetwork or simply an ad hoc communication between at least two devices.

Wi-Fi can allow connection to the Internet from a couch at home, a bedin a hotel room or a conference room at work, without wires. Wi-Fi is awireless technology similar to that used in a cell phone that enablessuch devices, e.g., computers, to send and receive data indoors and out;anywhere within the range of a base station. Wi-Fi networks use radiotechnologies called IEEE 802.11 (a, b, g, n, ac, ag, etc.) to providesecure, reliable, fast wireless connectivity. A Wi-Fi network can beused to connect computers to each other, to the Internet, and to wirednetworks (which can use IEEE 802.3 or Ethernet). Wi-Fi networks operatein the unlicensed 2.4 and 5 GHz radio bands for example or with productsthat contain both bands (dual band), so the networks can providereal-world performance similar to the basic 10 BaseT wired Ethernetnetworks used in many offices.

Turning now to FIG. 5, an embodiment 500 of a mobile network platform510 is shown that is an example of network elements 150, 152, 154, 156,and/or VNEs 330, 332, 334, etc. For example, platform 510 can facilitatein whole or in part computing a capacity for each cell of a plurality ofcells associated with a network, responsive to determining that autilization of wireless spectrum associated with a first plurality ofclasses of traffic in the network is greater than a first threshold,upgrading a capacity in the network, and responsive to determining thatthe utilization of the wireless spectrum associated with the firstplurality of classes of traffic in the network is less than or equal tothe first threshold: computing a capacity for a second plurality ofclasses of traffic for each cell of the plurality of cells in accordancewith the capacity for each cell, performing analytical modeling orengaging a simulation to determine a throughput for each of the secondplurality of classes of traffic for each cell of the plurality of cellsin accordance with the computing of the capacity for the secondplurality of classes of traffic, and responsive to determining that thethroughput for at least one of the second plurality of classes oftraffic for at least one cell of the plurality of cells is less than asecond threshold, upgrading the capacity in the network, wherein theupgrading of the capacity in the network comprises one of: deploying anew cell at a predetermined level of wireless spectrum, wherein the newcell is not included in the plurality of cells, or increasing a wirelessspectrum allocation of a first cell of the plurality of cells. Platform510 can facilitate in whole or in part responsive to determining that autilization of wireless spectrum associated with a first class oftraffic in a network is greater than a first threshold, performing anupgrade of a capacity in the network, and responsive to determining thatthe utilization of the wireless spectrum associated with the first classof traffic in the network is less than or equal to the first threshold:computing a capacity for a second class of traffic for each cell of aplurality of cells of the network, performing analytical modeling orengaging a simulation to determine a throughput for the second class oftraffic for each cell of the plurality of cells in accordance with thecomputing of the capacity for the second class of traffic, andresponsive to determining that the throughput for the second class oftraffic for at least one cell of the plurality of cells is less than asecond threshold, performing the upgrade of the capacity in the network,wherein the performing of the upgrade of the capacity in the networkcomprises one of: deploying a new cell at a predetermined level ofwireless spectrum, wherein the new cell is not included in the pluralityof cells, or increasing a wireless spectrum allocation of a first cellof the plurality of cells. Platform 510 can facilitate in whole or inpart determining whether a utilization of wireless spectrum associatedwith a guaranteed class of traffic in a network is greater than a firstthreshold, responsive to the determining indicating that the utilizationof the wireless spectrum associated with the guaranteed class of trafficis greater than the first threshold, causing an upgrade of a capacity inthe network, and responsive to the determining indicating that theutilization of the wireless spectrum associated with the guaranteedclass of traffic is not greater than the first threshold: determining athroughput for a non-guaranteed class of traffic for each cell of aplurality of cells of the network, and responsive to determining thatthe throughput for the non-guaranteed class of traffic for at least onecell of the plurality of cells is less than a second threshold, causingthe upgrade of the capacity in the network, wherein the causing of theupgrade of the capacity in the network comprises: deploying a new cellat a predetermined level of wireless spectrum, wherein the new cell isnot included in the plurality of cells, increasing a wireless spectrumallocation of a first cell of the plurality of cells, or a combinationthereof.

In one or more embodiments, the mobile network platform 510 can generateand receive signals transmitted and received by base stations or accesspoints such as base station or access point 122. Generally, mobilenetwork platform 510 can comprise components, e.g., nodes, gateways,interfaces, servers, or disparate platforms, that facilitate bothpacket-switched (PS) (e.g., internet protocol (IP), frame relay,asynchronous transfer mode (ATM)) and circuit-switched (CS) traffic(e.g., voice and data), as well as control generation for networkedwireless telecommunication. As a non-limiting example, mobile networkplatform 510 can be included in telecommunications carrier networks, andcan be considered carrier-side components as discussed elsewhere herein.Mobile network platform 510 comprises CS gateway node(s) 512 which caninterface CS traffic received from legacy networks like telephonynetwork(s) 540 (e.g., public switched telephone network (PSTN), orpublic land mobile network (PLMN)) or a signaling system #7 (SS7)network 560. CS gateway node(s) 512 can authorize and authenticatetraffic (e.g., voice) arising from such networks. Additionally, CSgateway node(s) 512 can access mobility, or roaming, data generatedthrough SS7 network 560; for instance, mobility data stored in a visitedlocation register (VLR), which can reside in memory 530. Moreover, CSgateway node(s) 512 interfaces CS-based traffic and signaling and PSgateway node(s) 518. As an example, in a 3GPP UMTS network, CS gatewaynode(s) 512 can be realized at least in part in gateway GPRS supportnode(s) (GGSN). It should be appreciated that functionality and specificoperation of CS gateway node(s) 512, PS gateway node(s) 518, and servingnode(s) 516, is provided and dictated by radio technology(ies) utilizedby mobile network platform 510 for telecommunication over a radio accessnetwork 520 with other devices, such as a radiotelephone 575.

In addition to receiving and processing CS-switched traffic andsignaling, PS gateway node(s) 518 can authorize and authenticatePS-based data sessions with served mobile devices. Data sessions cancomprise traffic, or content(s), exchanged with networks external to themobile network platform 510, like wide area network(s) (WANs) 550,enterprise network(s) 570, and service network(s) 580, which can beembodied in local area network(s) (LANs), can also be interfaced withmobile network platform 510 through PS gateway node(s) 518. It is to benoted that WANs 550 and enterprise network(s) 570 can embody, at leastin part, a service network(s) like IP multimedia subsystem (IMS). Basedon radio technology layer(s) available in technology resource(s) orradio access network 520, PS gateway node(s) 518 can generate packetdata protocol contexts when a data session is established; other datastructures that facilitate routing of packetized data also can begenerated. To that end, in an aspect, PS gateway node(s) 518 cancomprise a tunnel interface (e.g., tunnel termination gateway (TTG) in3GPP UMTS network(s) (not shown)) which can facilitate packetizedcommunication with disparate wireless network(s), such as Wi-Finetworks.

In embodiment 500, mobile network platform 510 also comprises servingnode(s) 516 that, based upon available radio technology layer(s) withintechnology resource(s) in the radio access network 520, convey thevarious packetized flows of data streams received through PS gatewaynode(s) 518. It is to be noted that for technology resource(s) that relyprimarily on CS communication, server node(s) can deliver trafficwithout reliance on PS gateway node(s) 518; for example, server node(s)can embody at least in part a mobile switching center. As an example, ina 3GPP UMTS network, serving node(s) 516 can be embodied in serving GPRSsupport node(s) (SGSN).

For radio technologies that exploit packetized communication, server(s)514 in mobile network platform 510 can execute numerous applicationsthat can generate multiple disparate packetized data streams or flows,and manage (e.g., schedule, queue, format . . . ) such flows. Suchapplication(s) can comprise add-on features to standard services (forexample, provisioning, billing, customer support . . . ) provided bymobile network platform 510. Data streams (e.g., content(s) that arepart of a voice call or data session) can be conveyed to PS gatewaynode(s) 518 for authorization/authentication and initiation of a datasession, and to serving node(s) 516 for communication thereafter. Inaddition to application server, server(s) 514 can comprise utilityserver(s), a utility server can comprise a provisioning server, anoperations and maintenance server, a security server that can implementat least in part a certificate authority and firewalls as well as othersecurity mechanisms, and the like. In an aspect, security server(s)secure communication served through mobile network platform 510 toensure network's operation and data integrity in addition toauthorization and authentication procedures that CS gateway node(s) 512and PS gateway node(s) 518 can enact. Moreover, provisioning server(s)can provision services from external network(s) like networks operatedby a disparate service provider; for instance, WAN 550 or GlobalPositioning System (GPS) network(s) (not shown). Provisioning server(s)can also provision coverage through networks associated to mobilenetwork platform 510 (e.g., deployed and operated by the same serviceprovider), such as the distributed antennas networks shown in FIG. 1(s)that enhance wireless service coverage by providing more networkcoverage.

It is to be noted that server(s) 514 can comprise one or more processorsconfigured to confer at least in part the functionality of mobilenetwork platform 510. To that end, the one or more processor can executecode instructions stored in memory 530, for example. It is should beappreciated that server(s) 514 can comprise a content manager, whichoperates in substantially the same manner as described hereinbefore.

In example embodiment 500, memory 530 can store information related tooperation of mobile network platform 510. Other operational informationcan comprise provisioning information of mobile devices served throughmobile network platform 510, subscriber databases; applicationintelligence, pricing schemes, e.g., promotional rates, flat-rateprograms, couponing campaigns; technical specification(s) consistentwith telecommunication protocols for operation of disparate radio, orwireless, technology layers; and so forth. Memory 530 can also storeinformation from at least one of telephony network(s) 540, WAN 550, SS7network 560, or enterprise network(s) 570. In an aspect, memory 530 canbe, for example, accessed as part of a data store component or as aremotely connected memory store.

In order to provide a context for the various aspects of the disclosedsubject matter, FIG. 5, and the following discussion, are intended toprovide a brief, general description of a suitable environment in whichthe various aspects of the disclosed subject matter can be implemented.While the subject matter has been described above in the general contextof computer-executable instructions of a computer program that runs on acomputer and/or computers, those skilled in the art will recognize thatthe disclosed subject matter also can be implemented in combination withother program modules. Generally, program modules comprise routines,programs, components, data structures, etc. that perform particulartasks and/or implement particular abstract data types.

Turning now to FIG. 6, an illustrative embodiment of a communicationdevice 600 is shown. The communication device 600 can serve as anillustrative embodiment of devices such as data terminals 114, mobiledevices 124, vehicle 126, display devices 144 or other client devicesfor communication via either communications network 125. For example,computing device 600 can facilitate in whole or in part computing acapacity for each cell of a plurality of cells associated with anetwork, responsive to determining that a utilization of wirelessspectrum associated with a first plurality of classes of traffic in thenetwork is greater than a first threshold, upgrading a capacity in thenetwork, and responsive to determining that the utilization of thewireless spectrum associated with the first plurality of classes oftraffic in the network is less than or equal to the first threshold:computing a capacity for a second plurality of classes of traffic foreach cell of the plurality of cells in accordance with the capacity foreach cell, performing analytical modeling or engaging a simulation todetermine a throughput for each of the second plurality of classes oftraffic for each cell of the plurality of cells in accordance with thecomputing of the capacity for the second plurality of classes oftraffic, and responsive to determining that the throughput for at leastone of the second plurality of classes of traffic for at least one cellof the plurality of cells is less than a second threshold, upgrading thecapacity in the network, wherein the upgrading of the capacity in thenetwork comprises one of: deploying a new cell at a predetermined levelof wireless spectrum, wherein the new cell is not included in theplurality of cells, or increasing a wireless spectrum allocation of afirst cell of the plurality of cells. Computing device 600 canfacilitate in whole or in part responsive to determining that autilization of wireless spectrum associated with a first class oftraffic in a network is greater than a first threshold, performing anupgrade of a capacity in the network, and responsive to determining thatthe utilization of the wireless spectrum associated with the first classof traffic in the network is less than or equal to the first threshold:computing a capacity for a second class of traffic for each cell of aplurality of cells of the network, performing analytical modeling orengaging a simulation to determine a throughput for the second class oftraffic for each cell of the plurality of cells in accordance with thecomputing of the capacity for the second class of traffic, andresponsive to determining that the throughput for the second class oftraffic for at least one cell of the plurality of cells is less than asecond threshold, performing the upgrade of the capacity in the network,wherein the performing of the upgrade of the capacity in the networkcomprises one of: deploying a new cell at a predetermined level ofwireless spectrum, wherein the new cell is not included in the pluralityof cells, or increasing a wireless spectrum allocation of a first cellof the plurality of cells. Computing device 600 can facilitate in wholeor in part determining whether a utilization of wireless spectrumassociated with a guaranteed class of traffic in a network is greaterthan a first threshold, responsive to the determining indicating thatthe utilization of the wireless spectrum associated with the guaranteedclass of traffic is greater than the first threshold, causing an upgradeof a capacity in the network, and responsive to the determiningindicating that the utilization of the wireless spectrum associated withthe guaranteed class of traffic is not greater than the first threshold:determining a throughput for a non-guaranteed class of traffic for eachcell of a plurality of cells of the network, and responsive todetermining that the throughput for the non-guaranteed class of trafficfor at least one cell of the plurality of cells is less than a secondthreshold, causing the upgrade of the capacity in the network, whereinthe causing of the upgrade of the capacity in the network comprises:deploying a new cell at a predetermined level of wireless spectrum,wherein the new cell is not included in the plurality of cells,increasing a wireless spectrum allocation of a first cell of theplurality of cells, or a combination thereof.

The communication device 600 can comprise a wireline and/or wirelesstransceiver 602 (herein transceiver 602), a user interface (UI) 604, apower supply 614, a location receiver 616, a motion sensor 618, anorientation sensor 620, and a controller 606 for managing operationsthereof. The transceiver 602 can support short-range or long-rangewireless access technologies such as Bluetooth®, ZigBee®, WiFi, DECT, orcellular communication technologies, just to mention a few (Bluetooth®and ZigBee® are trademarks registered by the Bluetooth® Special InterestGroup and the ZigBee® Alliance, respectively). Cellular technologies caninclude, for example, CDMA-1×, UMTS/HSDPA, GSM/GPRS, TDMA/EDGE, EV/DO,WiMAX, SDR, LTE, as well as other next generation wireless communicationtechnologies as they arise. The transceiver 602 can also be adapted tosupport circuit-switched wireline access technologies (such as PSTN),packet-switched wireline access technologies (such as TCP/IP, VoIP,etc.), and combinations thereof.

The UI 604 can include a depressible or touch-sensitive keypad 608 witha navigation mechanism such as a roller ball, a joystick, a mouse, or anavigation disk for manipulating operations of the communication device600. The keypad 608 can be an integral part of a housing assembly of thecommunication device 600 or an independent device operably coupledthereto by a tethered wireline interface (such as a USB cable) or awireless interface supporting for example Bluetooth®. The keypad 608 canrepresent a numeric keypad commonly used by phones, and/or a QWERTYkeypad with alphanumeric keys. The UI 604 can further include a display610 such as monochrome or color LCD (Liquid Crystal Display), OLED(Organic Light Emitting Diode) or other suitable display technology forconveying images to an end user of the communication device 600. In anembodiment where the display 610 is touch-sensitive, a portion or all ofthe keypad 608 can be presented by way of the display 610 withnavigation features.

The display 610 can use touch screen technology to also serve as a userinterface for detecting user input. As a touch screen display, thecommunication device 600 can be adapted to present a user interfacehaving graphical user interface (GUI) elements that can be selected by auser with a touch of a finger. The display 610 can be equipped withcapacitive, resistive or other forms of sensing technology to detect howmuch surface area of a user's finger has been placed on a portion of thetouch screen display. This sensing information can be used to controlthe manipulation of the GUI elements or other functions of the userinterface. The display 610 can be an integral part of the housingassembly of the communication device 600 or an independent devicecommunicatively coupled thereto by a tethered wireline interface (suchas a cable) or a wireless interface.

The UI 604 can also include an audio system 612 that utilizes audiotechnology for conveying low volume audio (such as audio heard inproximity of a human ear) and high volume audio (such as speakerphonefor hands free operation). The audio system 612 can further include amicrophone for receiving audible signals of an end user. The audiosystem 612 can also be used for voice recognition applications. The UI604 can further include an image sensor 613 such as a charged coupleddevice (CCD) camera for capturing still or moving images.

The power supply 614 can utilize common power management technologiessuch as replaceable and rechargeable batteries, supply regulationtechnologies, and/or charging system technologies for supplying energyto the components of the communication device 600 to facilitatelong-range or short-range portable communications. Alternatively, or incombination, the charging system can utilize external power sources suchas DC power supplied over a physical interface such as a USB port orother suitable tethering technologies.

The location receiver 616 can utilize location technology such as aglobal positioning system (GPS) receiver capable of assisted GPS foridentifying a location of the communication device 600 based on signalsgenerated by a constellation of GPS satellites, which can be used forfacilitating location services such as navigation. The motion sensor 618can utilize motion sensing technology such as an accelerometer, agyroscope, or other suitable motion sensing technology to detect motionof the communication device 600 in three-dimensional space. Theorientation sensor 620 can utilize orientation sensing technology suchas a magnetometer to detect the orientation of the communication device600 (north, south, west, and east, as well as combined orientations indegrees, minutes, or other suitable orientation metrics).

The communication device 600 can use the transceiver 602 to alsodetermine a proximity to a cellular, WiFi, Bluetooth®, or other wirelessaccess points by sensing techniques such as utilizing a received signalstrength indicator (RSSI) and/or signal time of arrival (TOA) or time offlight (TOF) measurements. The controller 606 can utilize computingtechnologies such as a microprocessor, a digital signal processor (DSP),programmable gate arrays, application specific integrated circuits,and/or a video processor with associated storage memory such as Flash,ROM, RAM, SRAM, DRAM or other storage technologies for executingcomputer instructions, controlling, and processing data supplied by theaforementioned components of the communication device 600.

Other components not shown in FIG. 6 can be used in one or moreembodiments of the subject disclosure. For instance, the communicationdevice 600 can include a slot for adding or removing an identity modulesuch as a Subscriber Identity Module (SIM) card or Universal IntegratedCircuit Card (UICC). SIM or UICC cards can be used for identifyingsubscriber services, executing programs, storing subscriber data, and soon.

The terms “first,” “second,” “third,” and so forth, as used in theclaims, unless otherwise clear by context, is for clarity only anddoesn't otherwise indicate or imply any order in time. For instance, “afirst determination,” “a second determination,” and “a thirddetermination,” does not indicate or imply that the first determinationis to be made before the second determination, or vice versa, etc.

In the subject specification, terms such as “store,” “storage,” “datastore,” “data storage,” “database,” and substantially any otherinformation storage component relevant to operation and functionality ofa component, refer to “memory components,” or entities embodied in a“memory” or components comprising the memory. It will be appreciatedthat the memory components described herein can be either volatilememory or nonvolatile memory, or can comprise both volatile andnonvolatile memory, by way of illustration, and not limitation, volatilememory, non-volatile memory, disk storage, and memory storage. Further,nonvolatile memory can be included in read only memory (ROM),programmable ROM (PROM), electrically programmable ROM (EPROM),electrically erasable ROM (EEPROM), or flash memory. Volatile memory cancomprise random access memory (RAM), which acts as external cachememory. By way of illustration and not limitation, RAM is available inmany forms such as synchronous RAM (SRAM), dynamic RAM (DRAM),synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhancedSDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and direct Rambus RAM(DRRAIVI). Additionally, the disclosed memory components of systems ormethods herein are intended to comprise, without being limited tocomprising, these and any other suitable types of memory.

Moreover, it will be noted that the disclosed subject matter can bepracticed with other computer system configurations, comprisingsingle-processor or multiprocessor computer systems, mini-computingdevices, mainframe computers, as well as personal computers, hand-heldcomputing devices (e.g., PDA, phone, smartphone, watch, tabletcomputers, netbook computers, etc.), microprocessor-based orprogrammable consumer or industrial electronics, and the like. Theillustrated aspects can also be practiced in distributed computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network; however, some if not allaspects of the subject disclosure can be practiced on stand-alonecomputers. In a distributed computing environment, program modules canbe located in both local and remote memory storage devices.

In one or more embodiments, information regarding use of services can begenerated including services being accessed, media consumption history,user preferences, and so forth. This information can be obtained byvarious methods including user input, detecting types of communications(e.g., video content vs. audio content), analysis of content streams,sampling, and so forth. The generating, obtaining and/or monitoring ofthis information can be responsive to an authorization provided by theuser. In one or more embodiments, an analysis of data can be subject toauthorization from user(s) associated with the data, such as an opt-in,an opt-out, acknowledgement requirements, notifications, selectiveauthorization based on types of data, and so forth.

Some of the embodiments described herein can also employ artificialintelligence (AI) to facilitate automating one or more featuresdescribed herein. The embodiments (e.g., in connection withautomatically identifying acquired cell sites that provide a maximumvalue/benefit after addition to an existing communication network) canemploy various AI-based schemes for carrying out various embodimentsthereof. Moreover, the classifier can be employed to determine a rankingor priority of each cell site of the acquired network. A classifier is afunction that maps an input attribute vector, x=(x1, x2, x3, x4, xn), toa confidence that the input belongs to a class, that is, f(x)=confidence(class). Such classification can employ a probabilistic and/orstatistical-based analysis (e.g., factoring into the analysis utilitiesand costs) to determine or infer an action that a user desires to beautomatically performed. A support vector machine (SVM) is an example ofa classifier that can be employed. The SVM operates by finding ahypersurface in the space of possible inputs, which the hypersurfaceattempts to split the triggering criteria from the non-triggeringevents. Intuitively, this makes the classification correct for testingdata that is near, but not identical to training data. Other directedand undirected model classification approaches comprise, e.g., naïveBayes, Bayesian networks, decision trees, neural networks, fuzzy logicmodels, and probabilistic classification models providing differentpatterns of independence can be employed. Classification as used hereinalso is inclusive of statistical regression that is utilized to developmodels of priority.

As will be readily appreciated, one or more of the embodiments canemploy classifiers that are explicitly trained (e.g., via a generictraining data) as well as implicitly trained (e.g., via observing UEbehavior, operator preferences, historical information, receivingextrinsic information). For example, SVMs can be configured via alearning or training phase within a classifier constructor and featureselection module. Thus, the classifier(s) can be used to automaticallylearn and perform a number of functions, including but not limited todetermining according to predetermined criteria which of the acquiredcell sites will benefit a maximum number of subscribers and/or which ofthe acquired cell sites will add minimum value to the existingcommunication network coverage, etc.

As used in some contexts in this application, in some embodiments, theterms “component,” “system” and the like are intended to refer to, orcomprise, a computer-related entity or an entity related to anoperational apparatus with one or more specific functionalities, whereinthe entity can be either hardware, a combination of hardware andsoftware, software, or software in execution. As an example, a componentmay be, but is not limited to being, a process running on a processor, aprocessor, an object, an executable, a thread of execution,computer-executable instructions, a program, and/or a computer. By wayof illustration and not limitation, both an application running on aserver and the server can be a component. One or more components mayreside within a process and/or thread of execution and a component maybe localized on one computer and/or distributed between two or morecomputers. In addition, these components can execute from variouscomputer readable media having various data structures stored thereon.The components may communicate via local and/or remote processes such asin accordance with a signal having one or more data packets (e.g., datafrom one component interacting with another component in a local system,distributed system, and/or across a network such as the Internet withother systems via the signal). As another example, a component can be anapparatus with specific functionality provided by mechanical partsoperated by electric or electronic circuitry, which is operated by asoftware or firmware application executed by a processor, wherein theprocessor can be internal or external to the apparatus and executes atleast a part of the software or firmware application. As yet anotherexample, a component can be an apparatus that provides specificfunctionality through electronic components without mechanical parts,the electronic components can comprise a processor therein to executesoftware or firmware that confers at least in part the functionality ofthe electronic components. While various components have beenillustrated as separate components, it will be appreciated that multiplecomponents can be implemented as a single component, or a singlecomponent can be implemented as multiple components, without departingfrom example embodiments.

Further, the various embodiments can be implemented as a method,apparatus or article of manufacture using standard programming and/orengineering techniques to produce software, firmware, hardware or anycombination thereof to control a computer to implement the disclosedsubject matter. The term “article of manufacture” as used herein isintended to encompass a computer program accessible from anycomputer-readable device or computer-readable storage/communicationsmedia. For example, computer readable storage media can include, but arenot limited to, magnetic storage devices (e.g., hard disk, floppy disk,magnetic strips), optical disks (e.g., compact disk (CD), digitalversatile disk (DVD)), smart cards, and flash memory devices (e.g.,card, stick, key drive). Of course, those skilled in the art willrecognize many modifications can be made to this configuration withoutdeparting from the scope or spirit of the various embodiments.

In addition, the words “example” and “exemplary” are used herein to meanserving as an instance or illustration. Any embodiment or designdescribed herein as “example” or “exemplary” is not necessarily to beconstrued as preferred or advantageous over other embodiments ordesigns. Rather, use of the word example or exemplary is intended topresent concepts in a concrete fashion. As used in this application, theterm “or” is intended to mean an inclusive “or” rather than an exclusive“or”. That is, unless specified otherwise or clear from context, “Xemploys A or B” is intended to mean any of the natural inclusivepermutations. That is, if X employs A; X employs B; or X employs both Aand B, then “X employs A or B” is satisfied under any of the foregoinginstances. In addition, the articles “a” and “an” as used in thisapplication and the appended claims should generally be construed tomean “one or more” unless specified otherwise or clear from context tobe directed to a singular form.

Moreover, terms such as “user equipment,” “mobile station,” “mobile,”“subscriber station,” “access terminal,” “terminal,” “handset,” “mobiledevice” (and/or terms representing similar terminology) can refer to awireless device utilized by a subscriber or user of a wirelesscommunication service to receive or convey data, control, voice, video,sound, gaming or substantially any data-stream or signaling-stream. Theforegoing terms are utilized interchangeably herein and with referenceto the related drawings.

Furthermore, the terms “user,” “subscriber,” “customer,” “consumer” andthe like are employed interchangeably throughout, unless contextwarrants particular distinctions among the terms. It should beappreciated that such terms can refer to human entities or automatedcomponents supported through artificial intelligence (e.g., a capacityto make inference based, at least, on complex mathematical formalisms),which can provide simulated vision, sound recognition and so forth.

As employed herein, the term “processor” can refer to substantially anycomputing processing unit or device comprising, but not limited tocomprising, single-core processors; single-processors with softwaremultithread execution capability; multi-core processors; multi-coreprocessors with software multithread execution capability; multi-coreprocessors with hardware multithread technology; parallel platforms; andparallel platforms with distributed shared memory. Additionally, aprocessor can refer to an integrated circuit, an application specificintegrated circuit (ASIC), a digital signal processor (DSP), a fieldprogrammable gate array (FPGA), a programmable logic controller (PLC), acomplex programmable logic device (CPLD), a discrete gate or transistorlogic, discrete hardware components or any combination thereof designedto perform the functions described herein. Processors can exploitnano-scale architectures such as, but not limited to, molecular andquantum-dot based transistors, switches and gates, in order to optimizespace usage or enhance performance of user equipment. A processor canalso be implemented as a combination of computing processing units.

As used herein, terms such as “data storage,” “data storage,”“database,” and substantially any other information storage componentrelevant to operation and functionality of a component, refer to “memorycomponents,” or entities embodied in a “memory” or components comprisingthe memory. It will be appreciated that the memory components orcomputer-readable storage media, described herein can be either volatilememory or nonvolatile memory or can include both volatile andnonvolatile memory.

What has been described above includes mere examples of variousembodiments. It is, of course, not possible to describe everyconceivable combination of components or methodologies for purposes ofdescribing these examples, but one of ordinary skill in the art canrecognize that many further combinations and permutations of the presentembodiments are possible. Accordingly, the embodiments disclosed and/orclaimed herein are intended to embrace all such alterations,modifications and variations that fall within the spirit and scope ofthe appended claims. Furthermore, to the extent that the term “includes”is used in either the detailed description or the claims, such term isintended to be inclusive in a manner similar to the term “comprising” as“comprising” is interpreted when employed as a transitional word in aclaim.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

As may also be used herein, the term(s) “operably coupled to”, “coupledto”, and/or “coupling” includes direct coupling between items and/orindirect coupling between items via one or more intervening items. Suchitems and intervening items include, but are not limited to, junctions,communication paths, components, circuit elements, circuits, functionalblocks, and/or devices. As an example of indirect coupling, a signalconveyed from a first item to a second item may be modified by one ormore intervening items by modifying the form, nature or format ofinformation in a signal, while one or more elements of the informationin the signal are nevertheless conveyed in a manner than can berecognized by the second item. In a further example of indirectcoupling, an action in a first item can cause a reaction on the seconditem, as a result of actions and/or reactions in one or more interveningitems.

Although specific embodiments have been illustrated and describedherein, it should be appreciated that any arrangement which achieves thesame or similar purpose may be substituted for the embodiments describedor shown by the subject disclosure. The subject disclosure is intendedto cover any and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, can be used in the subject disclosure.For instance, one or more features from one or more embodiments can becombined with one or more features of one or more other embodiments. Inone or more embodiments, features that are positively recited can alsobe negatively recited and excluded from the embodiment with or withoutreplacement by another structural and/or functional feature. The stepsor functions described with respect to the embodiments of the subjectdisclosure can be performed in any order. The steps or functionsdescribed with respect to the embodiments of the subject disclosure canbe performed alone or in combination with other steps or functions ofthe subject disclosure, as well as from other embodiments or from othersteps that have not been described in the subject disclosure. Further,more than or less than all of the features described with respect to anembodiment can also be utilized.

What is claimed is:
 1. A device, comprising: a processing systemincluding a processor; and a memory that stores executable instructionsthat, when executed by the processing system, facilitate performance ofoperations, the operations comprising: computing a capacity for eachcell of a plurality of cells associated with a network; computing acapacity for a plurality of classes of traffic for each cell of theplurality of cells in accordance with the capacity for each cell;determining a throughput for each of the plurality of classes of trafficfor each cell of the plurality of cells in accordance with the computingof the capacity for the plurality of classes of traffic; and responsiveto determining that the throughput for at least one of the plurality ofclasses of traffic for at least one cell of the plurality of cells isless than a first threshold, upgrading a capacity in the network,wherein the upgrading of the capacity in the network comprises at leastone of: deploying a new cell at a predetermined level of wirelessspectrum, wherein the new cell is not included in the plurality ofcells; or increasing a wireless spectrum allocation of a first cell ofthe plurality of cells.
 2. The device of claim 1, wherein the pluralityof classes of traffic includes buffered video traffic, email traffic,text traffic, file transfer traffic, peer-to-peer file sharing traffic,progressive video traffic, interactive gaming traffic, or anycombination thereof.
 3. The device of claim 1, wherein the computing ofthe capacity for the plurality of classes of traffic for each cell ofthe plurality of cells is further in accordance with a plurality ofprojections associated with a demand for a second plurality of classesof traffic and a plurality of bandwidths occupied by each session of thesecond plurality of classes of traffic.
 4. The device of claim 1,wherein the upgrading of the capacity in the network comprises theincreasing of the wireless spectrum allocation of the first cell.
 5. Thedevice of claim 1, wherein the upgrading of the capacity in the networkfurther comprises: determining that the wireless spectrum allocation ofthe first cell is less than a second threshold.
 6. The device of claim5, wherein the upgrading of the capacity in the network furthercomprises: responsive to the determining that the wireless spectrumallocation of the first cell is less than the second threshold,performing the increasing of the wireless spectrum allocation of thefirst cell.
 7. The device of claim 1, wherein the upgrading of thecapacity in the network further comprises: determining that the wirelessspectrum allocation of the first cell is greater than a secondthreshold.
 8. The device of claim 7, wherein the upgrading of thecapacity in the network further comprises: responsive to the determiningthat the wireless spectrum allocation of the first cell is greater thanthe second threshold, performing the deploying of the new cell at thepredetermined level of wireless spectrum.
 9. The device of claim 7,wherein the predetermined level of wireless spectrum is less than thesecond threshold.
 10. The device of claim 1, wherein the determining ofthe throughput for each of the plurality of classes of traffic for eachcell of the plurality of cells is based on modeling.
 11. The device ofclaim 10, wherein the modeling is based on an apportionment of a volumeof the plurality of classes of traffic in proportion to a ratio of thecapacity for each cell relative to a total capacity for all of theplurality of cells.
 12. The device of claim 1, wherein the determiningof the throughput for each of the plurality of classes of traffic foreach cell of the plurality of cells is based on an engaging of asimulation.
 13. The device of claim 12, wherein the engaging of thesimulation comprises: detecting an event corresponding to an arrival offirst traffic associated with a second plurality of classes of traffic,an arrival of second traffic associated with the plurality of classes oftraffic, a departure of third traffic associated with the secondplurality of classes of traffic, a departure of fourth trafficassociated with the plurality of classes of traffic, or any combinationthereof; and responsive to the detecting of the event, recording samplesof throughput.
 14. The device of claim 13, wherein the engaging of thesimulation further comprises: processing the samples by applying weightsto the samples to generate the throughput for each of the plurality ofclasses of traffic for each cell of the plurality of cells.
 15. Thedevice of claim 13, wherein the event includes the arrival of the firsttraffic, and wherein the engaging of the simulation further comprises:determining that blocking is not implemented with respect to the firsttraffic, wherein the recording of the samples of throughput is furtherresponsive to the determining that blocking is not implemented withrespect to the first traffic.
 16. The device of claim 1, wherein theupgrading of the capacity in the network comprises the deploying of thenew cell at the predetermined level of wireless spectrum.
 17. Anon-transitory machine-readable medium, comprising executableinstructions that, when executed by a processing system including aprocessor, facilitate performance of operations, the operationscomprising: computing a capacity for a class of traffic for each cell ofa plurality of cells of a network; determining a throughput for theclass of traffic for each cell of the plurality of cells in accordancewith the computing of the capacity for the class of traffic; andresponsive to determining that the throughput for the class of trafficfor at least one cell of the plurality of cells is less than athreshold, performing an upgrade of a capacity in the network, whereinthe performing of the upgrade of the capacity in the network comprisesat least one of: deploying a new cell at a predetermined level ofwireless spectrum, wherein the new cell is not included in the pluralityof cells; or increasing a wireless spectrum allocation of a first cellof the plurality of cells.
 18. A method, comprising: determining, by aprocessing system including a processor, a throughput for anon-guaranteed class of traffic for each cell of a plurality of cells ofa network; and responsive to determining, by the processing system, thatthe throughput for the non-guaranteed class of traffic for at least onecell of the plurality of cells is less than a threshold, causing anupgrade of a capacity in the network, wherein the causing of the upgradeof the capacity in the network comprises one or both of: deploying a newcell at a predetermined level of wireless spectrum, wherein the new cellis not included in the plurality of cells, and increasing a wirelessspectrum allocation of a first cell of the plurality of cells.
 19. Themethod of claim 18, wherein the causing of the upgrade of the capacityin the network comprises the deploying of the new cell at thepredetermined level of wireless spectrum.
 20. The method of claim 19,wherein the predetermined level corresponds to a minimum discrete levelwithin the new cell.